Data Architecture for Climate Risk Reports: Where Does the Data Come From and How Is It Managed?

Climate risk report preparation starts in the wrong place for most companies. First, a template is found, then a presentation is prepared, and finally data is requested. This sequence makes the report look good but doesn't build trust. Because a climate risk report is not about content creation—it's about establishing a data and decision framework.

TCFD (Task Force on Climate-related Financial Disclosures) and other frameworks require systematic assessment and disclosure of climate risks. This assessment cannot be done without a solid data infrastructure.

In this article, we cover the data architecture required for climate risk reports: what data is needed, where it's collected from, and how it's managed.

Climate risk categories

Climate risks are evaluated in two main categories:

Physical risks

Risks arising from the physical effects of climate change. It has two sub-categories:

Acute physical risks: Sudden and severe events. Such as floods, storms, wildfires, heat waves. Risk of direct damage to assets, operational disruption, and supply chain disruption.

Chronic physical risks: Long-term changes. Average temperature increase, sea level rise, precipitation pattern changes. Effects on agricultural yield, water availability, energy demand.

Transition risks

Risks arising from the transition to a low-carbon economy:

Policy and regulation risks: Carbon pricing, emission limits, energy efficiency requirements, reporting obligations.

Technology risks: Obsolescence of existing technologies, costs of transitioning to new technologies, R&D investment needs.

Market risks: Changes in consumer preferences, raw material price fluctuations, market share losses.

Reputation risks: Failure to meet stakeholder expectations, perception of inadequate climate action.

Data requirements for physical risk

Location data

The foundation of physical risk assessment is asset locations. The following information is required:

Geographic coordinates of all facilities (latitude, longitude)
Critical infrastructure locations (warehouses, data centers)
Supplier and customer locations (critical ones)
Transportation routes and logistics nodes

Data sources: Facility management, real estate, supply chain, operations.

Hazard data

Which climate hazards are you exposed to? Hazard exposure assessment is required for each location.

Hazard types:

Flood (river, coastal, stormwater)
Drought and water stress
Heat wave and extreme temperature
Storm and hurricane
Wildfire
Sea level rise

Hazard data comes from external sources:

Climate models (IPCC scenarios: SSP1-2.6, SSP2-4.5, SSP5-8.5)
National meteorological and hydrological institutions
Commercial climate data providers (Moody's, S&P, MSCI)
Open source databases (World Resources Institute, Climate Central)

Vulnerability data

The vulnerability of assets to hazards must be assessed:

Building and infrastructure characteristics (age, construction type, protection measures)
Critical equipment and systems
Insurance coverage and historical damage data
Operational continuity plans

Data sources: Facility management, risk management, insurance, operations.

Data requirements for transition risk

Regulatory data

Current and expected climate policies must be monitored:

Carbon pricing mechanisms (ETS, carbon tax)
Emission reduction targets and limits
Energy efficiency standards
Reporting and disclosure obligations
Sectoral regulations

Data sources: Government agencies, industry associations, consultancy reports, legal department.

Market data

Market dynamics and stakeholder expectations must be monitored:

Customer preferences and demand changes
Supplier expectations and requirements
Competitor strategies and positioning
Investor expectations

Data sources: Sales, marketing, procurement, strategy, investor relations.

Technology data

For planning the transition to low-carbon technologies:

Alternative technology costs and maturity levels
Transformation investment requirements
Technology roadmaps
R&D investments

Data sources: R&D, operations, engineering, strategy.

Emission data

Transition risk assessment requires the current emission profile:

Scope 1, 2, 3 emission inventory
Emission intensities (per unit product, revenue, etc.)
Reduction targets and progress

Data sources: Sustainability, operations, energy management.

Data for scenario analysis

TCFD recommends assessing strategic resilience under different climate scenarios. For scenario analysis:

Reference scenarios (IPCC, IEA, NGFS)
Company-specific assumptions
Financial modeling parameters
Time horizons (short, medium, long term)

Scenario analysis fundamentals

Scenario analysis evaluates how the company will be affected in different climate futures. Basic steps:

Scenario selection: At least two scenarios should be used. For example, 1.5°C (low emission) and 3-4°C (high emission) scenarios.
Time horizon determination: Short term (0-5 years), medium term (5-15 years), long term (15-30 years) distinction should be made.
Impact assessment: The impacts of physical and transition risks on the company under each scenario should be analyzed.
Strategic response: Strategic options should be developed to manage risks and evaluate opportunities.

Physical and transition risk examples for Turkey

Physical risk examples (Turkey context):

Increasing fire risk in Mediterranean and Aegean regions
Water stress and drought in Central Anatolia
Sea level rise risk for coastal facilities
Impact of heat waves on labor productivity

Transition risk examples (Turkey context):

Turkey ETS (Emissions Trading System) preparations and carbon pricing
EU CBAM (Carbon Border Adjustment Mechanism) impact on export sectors
Renewable energy targets and energy transformation requirements
Sustainability reporting obligations (CMB regulations)

Data ownership and governance

The following questions must be answered for each data set:

Who collects? Data collection responsibility should be determined
Who updates? Update frequency and responsibility should be defined
Who approves? Data quality approval process should be established
Where does it flow to and from? Data flow diagram should be drawn

Without clear data ownership, the climate risk report is prepared from scratch every year. It doesn't become institutionalized.

Data quality and gaps

Not all data will be perfect. Data gaps and quality issues must be documented:

Which data is available, which is missing?
Is data quality sufficient?
If proxy or estimation was used, what is the justification?
What is the improvement plan?

Transparency is more valuable than hiding data gaps.

Conclusion

A climate risk report is built on data quality and flow. Scenario analysis is not credible without data architecture. Different data sets are required for physical and transition risks. Source, ownership, and flow must be defined for each data set. Once this infrastructure is established, annual updates and reporting become much more efficient.

Data Architecture for Climate Risk Reports: Where Does the Data Come From and How Is It Managed?